Anisha Gehlot

ClinicQ — Real Time Hospital Queue Management App - project title

Small clinics manage patient queues manually — receptionists juggle walk-ins, urgent cases, and multiple doctors on paper. Patients have zero visibility into their wait time or position, leading to overcrowding at reception desks and frequent interruptions. There is no affordable real-time solution for clinics that cannot invest in expensive hospital software. - ps Process I started by identifying the core pain: patients don't know their queue position, and receptionists have no digital tool. I chose React for the UI and Firebase Realtime Database for instant sync across devices — no backend server needed. I designed 4 screens: Receptionist (add/manage patients), Patient (live queue view), Status (now serving display), and Analytics (wait time tracking). I faced a challenge with urgent patient prioritization — solved by sorting the queue array by urgency flag first, then timestamp. I also hit a CI/CD issue where ESLint warnings blocked Netlify deployment, which I debugged and fixed. Each screen updates in real time via Firebase onValue() listeners — no refresh needed. - your process Results The project successfully streamlined the patient queue management process by organizing patients doctor-wise and providing real-time queue updates. Receptionists can efficiently add, update, and manage patient records, while patients can instantly check their status using their token number or name. The analytics dashboard provides valuable insights such as total patients handled by each doctor, average consultation time, patients served per hour, and the number of waiting and completed patients. These features improved operational efficiency, reduced manual effort, enhanced transparency - res Reflection If I had more time, I would enhance the system by adding online appointment booking, SMS/email notifications, and role-based authentication for doctors, receptionists, and administrators. I would also improve the analytics dashboard with advanced reports, charts, and patient trend analysis. Additionally, I would implement AI-based waiting time prediction to provide more accurate queue estimates for patients. These improvements would make the system more scalable, user-friendly, and suitable for larger healthcare environments. - what if i do differently